• Jayesh yadav
• Jitendra Dangra

Glaucoma is a disorder that affects human vision and is caused by damage to the optic nerve. This illness is referred to as the ailment that cannot be reversed and ultimately results in vision impairment. A significant number of deep learning (DL) models have been developed up until this point for the purpose of accurately identifying glaucoma. As a result, we have demonstrated here an architecture for the accurate diagnosis of glaucoma based on deep learning and making use of convolutional neural networks (CNN). With the assistance of CNN, a distinction may be made between the patterns that are produced for glaucoma and those that are developed for people who do not have glaucoma. CNN provides a hierarchical structure of the photos so that they can be differentiated. There are a total of six different levels that can be used to review proposed work. In this study, we also utilized the dropout strategy in order to achieve optimal performance in the glaucoma diagnostic process. The SCES and the ORIGA were both used as datasets in the studies that were carried out. When the proposed method was used to the ORIGA dataset, accuracy values of 99.12 percent were attained, and accuracy values of 99.37 percent were obtained when the method was applied to the SCES dataset. During the examination, in which we made use of the most recent and cutting-edge techniques, we found that the accuracy of ORIGA was 86%, while the accuracy of the SCES datase was 91%.

Glaucoma Detection, Deep Learning, Convolutional Neural Network, Glaucoma Prediction.

"An Application of Convolutional Neural Networks to the Detection of Glaucoma", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 9, page no.d155-d159, September-2022, Available :http://www.jetir.org/papers/JETIR2209320.pdf

"An Application of Convolutional Neural Networks to the Detection of Glaucoma", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 9, page no. ppd155-d159, September-2022, Available at : http://www.jetir.org/papers/JETIR2209320.pdf